Research Scientist, Personalization & Recommendations

Hook Music; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

Hook Music Inc. is seeking a Research Scientist to design and advance state-of-the-art recommendation systems at scale. You will translate research into production-ready models and partner with engineering and product teams.

You’ll tackle ranking, personalization, user modeling, exploration-exploitation, and representation learning using real-world data to drive measurable impact. Join a team that values experimentation, rigorous evaluation, and clear communication of results to both technical

Qualifications

• Advanced degree (PhD or MS) in Computer Science, Machine Learning, Statistics, or a related field, or equivalent experience.
• Experience developing recommendation, ranking, or personalization models for user-generated content.
• Strong foundation in machine learning, statistics, and data analysis.
• Proficiency in Python and modern ML frameworks (e.g., PyTorch).
• Ability to conduct independent research and collaborate effectively in team environments.

Responsibilities

• Design, implement, and evaluate machine learning models for recommendation, ranking, and personalization
• Conduct applied research to improve model quality, robustness, and efficiency
• Develop and execute experimentation strategies using offline evaluation and online testing
• Work with complex datasets to understand user behavior and system performance
• Collaborate with engineering and product teams to transition research into production systems
• Share research results in clear, accessible ways with both technical and non-technical audiences
• Stay informed about advances in recommendation systems and related ML methodologies

Skills

Recommendation systems

Personalization

Ranking models

Python

PyTorch

Independent research

Team collaboration

Education

MS or PhD in CS/ML/Statistics or related

Tools

PyTorch

Job description

Hook Music Inc. is seeking a Research Scientist to design and advance state-of-the-art recommendation systems at scale. You will translate research into production-ready models and partner with engineering and product teams.

You’ll tackle ranking, personalization, user modeling, exploration-exploitation, and representation learning using real-world data to drive measurable impact. Join a team that values experimentation, rigorous evaluation, and clear communication of results to both technical